PhD Chapter 3

Results 1/3

This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.0.4.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

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Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Dredge FishDred 2010-2014 21 Mactromeris polynyma
Net FishNet 2010 5 Clupea harengus, Gadus morhua
Trap FishTrap 2010-2015 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl FishTraw 2013-2014 2 Pandalus borealis

1. Maps

1.1. General map

1.2. Parameters maps

Maps of abiotic habitat variables:

Depth

Slope

River CDOM

2. Modelling the influence of human activities

We computed an exposure index for each human activity, which we will use for regression models and SDM (see section 2). Two categories of exposure index were calculated seperately: one for land- and sea-based activities and one for fisheries. These indices are relative, with a variation between 0 (low exposure) to 1 (high exposure).

A cumulative exposure index has been calculated by adding index of exposure of each activity.

2.1. Individual indices of exposure

2.1.1. Land- and sea-based activities

The following map present the sources of land- and sea-based human activities considered here. For each activity, we calculated an index of exposure \(E_{ij}\).

Methodology
Rationale

To calculate the exposure index \(E_{ij}\), we modelled the diffusion of theoretical particles in the ecosystem. Particles are the resultant of an activity, such as contaminants or sediment, and they diffuse from the source of the activity. This method is not based on any circulation model, because it is not yet available in the Baie des Sept Îles area.

Initially, three types of particles were considered, in order to produce different diffusion patterns. Combination of these responses allowed to represent each activity by a unique signature. The three types of particle considered were:

  • high diffusion particles (< 4 µm): e.g. clay, dissolved organic matter, small bacteria, viruses, chemical components, proteins
  • medium diffusion particles (between 4 µm and 2 mm): e.g. sand, silt, small particulate organic matter, large bacteria, phytoplankton, zooplankton
  • low diffusion particles (> 2 mm): e.g. gravel, large particulate organic matter, clusters of dead organisms, large zooplankton, larger organisms

After discussion with the committee during the PhD defense, we decided that this method introduced too much subjectivity in the calculation of exposure, in particular for particule composition coefficients. Thus, we changed the approach by considering only one type of particle with different maximum exposure ranges for each activity.

The goal of this method is to model the density of particles after diffusion from the source(s) of activity. An identical number of particles is released by each source of activity, from which they diffuse until a maximum range. Particle density is proportional to the distance from the source, i.e. a higher density will be found close to the source (and vice versa):

This density allows to give a final exposure score for each activity.

Computation

We simulated the dispersion of theoretical particles using the minimal distance from the source \(D\) and an inverse logarithm relationship, to account for a ‘journey’ in a 2D environment while reducing the contribution of highest values.

\(D_{ijk}\) has been calculated with a least-cost pathfinding algorithm from the R package gdistance. We first established a connectivity model based on a ‘resistance seascape’ concept. We created a 100 x 100 m raster whose cell can be selected to obtain the path connecting start (source of the activity) and end (each raster cell) points. Journey between two neighbour cells (chess queen configuration) has a cost to be included in this path, which is defined on several constraints to account for specific behaviour. The length of the final path then gives distance \(D\).

We considered four underlying principles for the physical constraints:

  • marine ecosystems: particles cannot disperse on land
  • gravity: particles disperse easily from shallow to deeper depths while the reverse is difficult
  • sedimentation: particles cannot disperse anymore when they have sedimented
  • hydrodynamism: particles disperse according to local hydrodynamical currents

⚠️ For now, the forth constraint is not yet implemented.

The connectivity model considered: (i) coasts as boundaries delimitating cells unselectable by the algorithm, (ii) bathymetry, (iii) a maximum sedimentation distance where cells beyond are unselectable, (iv) river plumes as hydrodynamical fronts with an intensity and a direction (a complete circulation model in BSI is not yet available). These constraints were included in the minimal distance calculation with the transition function \(f_{j}\) used by costDistance() when creating the least-cost path:

  • land is set with a connectivity of 0
  • bathymetry is compared between two cells:
    • when point A is shallower than point B, connectivity is high
    • when point A is deeper than point B, connectivity is low (with a certain diffusion)
  • cells beyond the cutoff threshold are set with a connectivity of 0
  • CDOM content (proxy of hydrodynamism) is compared between two cells (NOT IMPLEMENTED YET)

This alogrithm is constrained between the source and the maximum range for each activity. Range has been established based on literature information, and will be groundtruthed by experts:

AquaInf CityInf CityWha DredColl DredDump InduInf InduWha SewRain SewWast ShipMoor ShipTraf
500 500 5000 500 500 500 5000 2500 2500 1000 1000

The equation for \(D_{ij}\) is then:

\[ \left\{\begin{matrix} D_{ij} = f \left( B_{i}, H_{i} \right) & D_{ij} < R_{j} \\ D_{ij} = 0 & D_{ij} > R_{j} \end{matrix}\right. \]

  • \(f\) is the transition function
  • \(B_{i}\) is the bathymetry component
  • \(H_{i}\) is the hydrodynamics component
  • \(R_{j}\) is the range
  • \(i\) is a cell
  • \(j\) is a human activity

This allowed to calculate \(E_{ij}\) with the inverse logarithm relationship (plus one to avoid infinite values).

\[ E_{ij} = \frac{1}{ln(D_{ij} + 1)} \]

\(E_{ij}\) was standardized to vary within a scale between 0 and 1, with \(min(E_{ij})\) and \(max(E_{ij})\).

Results

The following maps present the values of \(E_{ij}\) for land/sea-based activities (grey = low exposure; dark blue = high exposure).

Aquaculture

City

Dredging

Industry

Sewers

Shipping

2.1.2. Fisheries

These data belong to Department of Fisheries and Oceans Canada, with a permission granted to David Beauchesne. As such, we cannot present raw products and we will work on derived data.

Here, \(E_{ij}\) have been calculated with a proxy based on fisheries data for each gear used in the area.

Methodology

We extracted data from a global database for the St. Lawrence, for all fishing events occuring within the Baie des Sept-Îles. Four types of gears (traps, bottom-trawls, nets and dredges) have been considered in the bay between 2010 and 2015. Eight species have been gathered (see table at the top of this page).

As each gear was not used consistently during this period, we averaged the number of fishing events to obtain a proxy of fishing intensity. Furthermore, we modified this proxy with a smoothing function in order to ‘diffuse’ the signal around the actual event.

Results

The following maps present the values of \(E_{ij}\) for fisheries (grey = low exposure; dark blue = high exposure):

Here are the maps for each gear:

Dredge

Net

Trap

Bottom-trawling

2.2. Cumulative exposure index

Here, we combine individual exposure indices into a unique value, the cumulative exposure index \(CE_{i}\), here with an additive relationship:

\[ CE_{i} = \sum_{j} E_{ij} \]

In future iterations, we will try different link functions to account for non-additive effects. This score varies between 0 and 7 (total number of considered human activities).

The cumulative exposure index has been represented in five classes, according to the colour code of the Marine Strategy Framework Directive (indigo = bad exposure, less than 20 %; crimson = high exposure, higher than 80 %).

This histogram represents the number of stations falling in each class:

These scores will be used for the species distribution models (see Section 3).

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